Optimization Software Benchmark

solar: A solar thermal power plant simulator for blackbox optimization benchmarking

  • Sébastien Le Digabel
  • Charles Audet
  • Xavier Lebeuf
  • Miguel Diago
  • Aïmen E. Gheribi
  • Christophe Tribes
    • Categories Constrained Nonlinear Optimization, Energy, Optimization Software Benchmark Tags , , ,

    This work introduces solar, a collection of  ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints)  differ across instances. Some are deterministic, others are stochastic … Read more

    Nonconvex optimization problems involving the Euclidean norm: Challenges, progress, and opportunities

  • Anatoliy Kuznetsov
  • Nikolaos Sahinidis
    • Categories Global Optimization, Optimization Software Benchmark Tags , , ,

    The field of global optimization has advanced significantly over the past three decades. Yet, the solution of even small instances of many nonconvex optimization problems involving the Euclidean norm to global optimality remains beyond the reach of modern global optimization methods. These problems include numerous well-known and high-impact open research questions from a diverse collection … Read more

    A Sequential Benders-based Mixed-Integer Quadratic Programming Algorithm

  • Andrea Ghezzi
  • Wim Van Roy
  • Sebastian Sager
  • Moritz Diehl
    • Categories (Mixed) Integer Nonlinear Programming, Applications - Science and Engineering, Optimization Software Benchmark Tags , , , ,

    For continuous decision spaces, nonlinear programs (NLPs) can be efficiently solved via sequential quadratic programming (SQP) and, more generally, sequential convex programming (SCP). These algorithms linearize only the nonlinear equality constraints and keep the outer convex structure of the problem intact, such as (conic) inequality constraints or convex objective terms. The aim of the presented … Read more

    Solving low-rank semidefinite programs via manifold optimization

  • Jie Wang
    • Categories Constrained Nonlinear Optimization, Optimization Software Benchmark, Semi-definite Programming Tags , , , , ,

    We propose a manifold optimization approach to solve linear semidefinite programs (SDP) with low-rank solutions. This approach incorporates the augmented Lagrangian method and the Burer-Monteiro factorization, and features the adaptive strategies for updating the factorization size and the penalty parameter. We prove that the present algorithm can solve SDPs to global optimality, despite of the … Read more

    Effective matrix adaptation strategy for noisy derivative-free optimization

  • Morteza Kimiaei
  • Arnold Neumaier
    • Categories Global Optimization Applications, Nonlinear Optimization, Optimization Software Benchmark Tags

    In this paper, we introduce a new effective matrix adaptation evolution strategy (MADFO) for noisy derivative-free optimization problems. Like every MAES solver, MADFO consists of three phases: mutation, selection and recombination. MADFO improves the mutation phase by generating good step sizes, neither too small nor too large, that increase the probability of selecting mutation points … Read more

    Orbital Crossover

  • Ethan Deakins
  • Bernard Knueven
  • James Ostrowski
    • Categories Convex Optimization, Linear Programming, Optimization Software Benchmark Tags , ,

    Symmetry in optimization has been known to wreak havoc in optimization algorithms. Often, some of the hardest instances are highly symmetric. This is not the case in linear programming, as symmetry allows one to reduce the size of the problem, possibly dramatically, while still maintaining the same optimal objective value. This is done by aggregating … Read more

    Efficient composite heuristics for integer bound constrained noisy optimization

  • Morteza Kimiaei
  • Arnold Neumaier
    • Categories Bound-constrained Optimization, Integer Programming, Optimization Software Benchmark Tags , , , ,

    This paper discusses a composite algorithm for bound constrained noisy derivative-free optimization problems with integer variables. This algorithm is an integer variant of the matrix adaptation evolution strategy. An integer derivative-free line search strategy along affine scaling matrix directions is used to generate candidate points. Each affine scaling matrix direction is a product of the … Read more

    OPM, a collection of Optimization Problems in Matlab

  • Serge Gratton
  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Optimization Software Benchmark, Unconstrained Optimization Tags , , ,

    OPM is a small collection of CUTEst unconstrained and bound-constrained nonlinear optimization problems, which can be used in Matlab for testing optimization algorithms directly (i.e. without installing additional software). Article Download View OPM, a collection of Optimization Problems in Matlab

    EETTlib – Energy-Efficient Train Timetabling Library

  • Andreas Bärmann
  • Patrick Gemander
  • Lukas Hager
  • Frederik Nöth
  • Oskar Schneider
    • Categories (Mixed) Integer Linear Programming, Optimization Software Benchmark, Scheduling Tags , , , , ,

    We introduce EETTlib, an instance library for the Energy-Efficient Train Timetabling problem. The task in this problem is to adjust a given timetable draft such that several key indicators relating to the energy consumption of the resulting railway traffic are minimized. These include peak power consumption, total energy consumption, loss in recuperation energy, fluctuation in … Read more

    An improved randomized algorithm with noise level tuning for large-scale noisy unconstrained DFO problems

  • Morteza Kimiaei
    • Categories Optimization Software Benchmark, Stochastic Programming, Unconstrained Optimization Tags , , , ,

    In this paper, a new randomized solver (called VRDFON) for noisy unconstrained derivative-free optimization (DFO) problems is discussed. Complexity result in the presence of noise for nonconvex functions is studied. Two effective ingredients of VRDFON are an improved derivative-free line search algorithm with many heuristic enhancements and quadratic models in adaptively determined subspaces. Numerical results … Read more